Artificial Intelligence (AI) and Machine Learning (ML) are transforming every industry—from healthcare and finance to transportation, education, and entertainment. AI/ML Using Python is a hands-on training program designed to introduce students to the core concepts of machine learning, data analysis, and intelligent systems using Python, one of the most popular and beginner-friendly programming languages for AI.
This 15-day hands-on training program is designed to introduce student’s essentials of data preprocessing, exploratory data analysis, training ML models, evaluating performance, and building simple neural networks. Through practical exercises and real-world datasets, students learn how machines make predictions, identify patterns, and generate insights from data. By the end of the training, learners will be able to design and implement complete AI/ML solutions using Python tools and libraries.
What you'll learn
Day 1&2 | Introduction to AI & Python for ML
- What is AI, ML, DL?
- Real-world applications
- Python setup (Anaconda)
- Basics of Python: variables, loops, functions
- Lists, dictionaries, sets
- File handling
Day 3&4| Python for Data Science & Data Preprocessing
- Installing libraries: NumPy, Pandas, Matplotlib
- Reading CSV files
- Data cleaning: missing values, duplicates
- Data transformation
- Encoding categorical values
- Normalization & scaling
Day 5&6 | Exploratory Data Analysis (EDA)
- Data statistics
- Visualizations: histograms, scatter plots, heatmaps
- Outliers and distributions
- Feature relationships
Day 7&8| Introduction to Machine Learning & Classification Algorithms
- Types of ML: Supervised vs Unsupervised
- Train-test split
- Evaluation metrics: accuracy, precision, recall
- Logistic Regression
- Decision Trees
- k-Nearest Neighbors
Day 9&10 | Regression & Model Evaluation
- Linear Regression (detailed)
- Polynomial regression
- Evaluating models: RMSE, MAE
- Clustering: K-Means
- Dimensionality reduction: PCA
- Applications of clustering
Day 11 | Intro to Neural Networks / Deep Learning
- Artificial neural network basics
- Using TensorFlow / Keras
- Building a simple ANN
Day 12 | Final Project + Presentations
- House price prediction (Regression)
- Email spam detection (Classification)
- Digit recognition using ANN
- (Students choose one from above)
- Explain the basics of Artificial Intelligence and Machine Learning Understand AI/ML concepts, types of learning, and real-world applications.
- Use Python effectively for data analysis and machine learning Apply Python programming and libraries such as NumPy, Pandas, Matplotlib, and scikit-learn.
- Preprocess and prepare real-world datasets for ML models Handle missing data, encode features, normalize data, and perform feature engineering.
- Perform Exploratory Data Analysis (EDA) Generate visualizations, identify patterns, and interpret data insights.
- Build and train machine learning models Implement regression, classification, and clustering algorithms using scikit-learn.
- Evaluate and compare model performance Use metrics like accuracy, precision, recall, confusion matrices, RMSE, etc.
- Create a basic Artificial Neural Network (ANN) Develop simple deep learning models using TensorFlow/Keras.
- Develop end-to-end AI/ML projects Work on real datasets, from data preprocessing to model deployment or presentation.
- Apply analytical thinking to solve data-driven problems Interpret results, optimize models, and propose solutions using ML techniques.
Demonstrate readiness for internships, projects, and higher studies in AI/ML present completed ML projects and use practical skills beneficial for placements and academic growth.
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1. Who is this AI/ML training program designed for?
2. 3. What programming background is needed to join this program?
3. What Python concepts are covered in the training?
4. What is data preprocessing and why is it important?
5. What types of machine learning algorithms are taught?
6. How are machine learning models evaluated?
7. What are neural networks and deep learning?
8. What skills will I gain after completing this program?
